Exploiting ChatGPT to simplify Italian bureaucratic and professional texts

Abstract

This paper investigates the use of ChatGPT, a large language model, for simplifying long sentences and nominal clusters in professional texts belonging to administrative and legal domains. We apply three prompt engineering techniques — zero-shot learning, few-shot learning, and Chain-of-Thought reasoning — to generate alternative sentences from a corpus of Italian texts. We evaluate the generated sentences using a survey with expert and non-expert readers of bureaucratic and legal Italian, focusing on ease of understanding, coherence, and preferences in rephrasing. Our results show that ChatGPT can effectively address the linguistic challenges outlined by UNI 11482:2013 Standard, and that complex prompting techniques yield better outcomes than simpler ones. We also discuss the implications of our findings for the optimization of text understanding and simplification using large language models.